Providing and managing an automated agent

Abstract

A system for providing and managing an automated agent. The automated agent may interact with a customer and utilize rules and instructions to determine a response to the customer and actions to perform. The customer interactions by the automated agent are driven by a set and/or subset of instructions that can be analyzed before each automated agent response or action. The subset of instructions, customer conversational input, and other content can be processed by a machine learning model, which may be implemented as a large language model (LLM).

1 . A method for providing an automated agent, comprising:
 receiving an inquiry from a customer by an automated agent on a first server;   retrieving instructions, based on the inquiry, for the automated agent from a remote data store;   determining, by a first machine learning model, which of the retrieved instructions are relevant to the inquiry;   providing the inquiry, the instructions, and a role to a second machine learning model, the role indicating a service level associated with the automated agent;   providing a response to the customer by the automated agent based on a response by the second machine learning model.   
     
     
         2 . The method of  claim 1 , wherein the instructions are received from a vector database. 
     
     
         3 . The method of  claim 1 , wherein the first machine learning model is a large language model. 
     
     
         4 . The method of  claim 3 , wherein determining which of the retrieved instructions are relevant further includes:
 constructing a prompt with the instructions, the inquiry, and a request to identify which of the retrieved instructions are relevant to satisfying the query; and   submitting the prompt to a large language model.   
     
     
         5 . The method of  claim 4 , wherein the instructions provided to the second machine learning model are instructions determined to be relevant by the first large language model. 
     
     
         6 . The method of  claim 1 , wherein the inquiry, the instructions, and the role are provided to the second machine learning model as prompt, the method further including:
 providing a plurality of prompts to the second large language model, each of the prompts including the inquiry, the instructions, the role, and a request to provide a single step in the process of solving the inquiry, the number of prompts sent to the second machine learning model equal to the number of steps required by the second machine learning model to solve the inquiry.   
     
     
         7 . The method of  claim 1 , wherein the instructions are automatically generated from training materials. 
     
     
         8 . A non-transitory computer readable storage medium having embodied thereon a program, the program being executable by a processor to perform a method for providing an automated agent, the method comprising:
 receiving an inquiry from a customer by an automated agent on a first server;   retrieving instructions, based on the inquiry, for the automated agent from a remote data store;   determining, by a first machine learning model, which of the retrieved instructions are relevant to the inquiry;   providing the inquiry, the instructions, and a role to a second machine learning model, the role indicating a service level associated with the automated agent;   providing a response to the customer by the automated agent based on a response by the second machine learning model.   
     
     
         9 . The non-transitory computer readable storage medium of  claim 8 , wherein the instructions are received from a vector database. 
     
     
         10 . The non-transitory computer readable storage medium of  claim 8 , wherein the first machine learning model is a large language model. 
     
     
         11 . The non-transitory computer readable storage medium of  claim 10 , wherein determining which of the retrieved instructions are relevant further includes:
 constructing a prompt with the instructions, the inquiry, and a request to identify which of the retrieved instructions are relevant to satisfying the query; and   submitting the prompt to a large language model.   
     
     
         12 . The non-transitory computer readable storage medium of  claim 11 , wherein the instructions provided to the second machine learning model are instructions determined to be relevant by the first large language model. 
     
     
         13 . The non-transitory computer readable storage medium of  claim 8 , wherein the inquiry, the instructions, and the role are provided to the second machine learning model as prompt, the method further including:
 providing a plurality of prompts to the second large language model, each of the prompts including the inquiry, the instructions, the role, and a request to provide a single step in the process of solving the inquiry, the number of prompts sent to the second machine learning model equal to the number of steps required by the second machine learning model to solve the inquiry.   
     
     
         14 . The non-transitory computer readable storage medium of  claim 8 , wherein the instructions are automatically generated from training materials. 
     
     
         15 . A system for providing an automated agent, comprising:
 one or more servers, wherein each server includes a memory and a processor; and   one or more modules stored in the memory and executed by at least one of the one or more processors to receive an inquiry from a customer by an automated agent on a first server, retrieving instructions, based on the inquiry, for the automated agent from a remote data store, determine, by a first machine learning model, which of the retrieved instructions are relevant to the inquiry, provide the inquiry, the instructions, and a role to a second machine learning model, the role indicating a service level associated with the automated agent, provide a response to the customer by the automated agent based on a response by the second machine learning model.   
     
     
         16 . The system of  claim 15 , wherein the instructions are received from a vector database. 
     
     
         17 . The system of  claim 15 , wherein the first machine learning model is a large language model. 
     
     
         18 . The system of  claim 17 , wherein determining which of the retrieved instructions are relevant further includes:
 constructing a prompt with the instructions, the inquiry, and a request to identify which of the retrieved instructions are relevant to satisfying the query; and   submitting the prompt to a large language model.   
     
     
         19 . The system of  claim 18 , wherein the instructions provided to the second machine learning model are instructions determined to be relevant by the first large language model. 
     
     
         20 . The system of  claim 15 , wherein the inquiry, the instructions, and the role are provided to the second machine learning model as a prompt, the one or more modules further executable to provide a plurality of prompts to the second large language model, each of the prompts including the inquiry, the instructions, the role, and a request to provide a single step in the process of solving the inquiry, the number of prompts sent to the second machine learning model equal to the number of steps required by the second machine learning model to solve the inquiry. 
     
     
         21 . The system of  claim 15 , wherein the instructions are automatically generated from training materials.